At Velocity, we believe that a strong team is built not only through collaboration at work but also through moments of connection and fun. Our recent Fun Friday celebration brought colleagues together for an exciting day filled with energy, laughter, and team spirit. Most days at Velocity move fast, deadlines, deliverables, back-to-back calls. Fun Friday…
What is GitHub CLI? GitHub CLI (gh) is the official command-line tool developed by GitHub that allows developers to manage repositories, pull requests, issues, workflows, and releases directly from the terminal. It simplifies GitHub operations without needing to switch between browser and terminal. Why Use GitHub CLI? System Requirements Before installing GitHub CLI on Ubuntu,…
GitHub Actions Runner Setup and Deployment Guide (Linux) Introduction GitHub Actions is a CI/CD platform that allows you to automate build, test, and deployment pipelines directly from your GitHub repository. A Self-Hosted Runner allows you to run GitHub workflows on your own server instead of GitHub’s cloud infrastructure. This is useful for: Why Use a…
Most B2B onboarding processes break somewhere between customer 50 and customer 100. Here are seven battle-tested customer onboarding automation patterns that scale without losing the human touch—plus the implementation architecture that makes them work.
Table of Contents Why the EU AI Act August 2026 Compliance Cliff Hits Engineering, Not Just Legal Gap 1: AI Act High-Risk System Classification You Can’t Query in Code Gap 2: Logging That Won’t Survive a Regulator’s Subpoena Gap 3: Human-in-the-Loop With No Real Veto Power Gap 4: AI Act Risk Management Without a Live…
Auditors found $4.2M in wrong approvals on a Fortune 500 IDP rollout. Here are the 7 production patterns — hybrid routing, cost-calibrated confidence, versioned schemas, tamper-evident audit chain, drift detection — that cut intelligent document processing cycle time 80% and net financial exposure 84%.
What’s in this guide Why AI agent output validation is the silent pipeline killer Pattern 1 — Schema-first prompting, not “return JSON” Pattern 2 — Two-stage parsing: shape, then semantics Pattern 3 — Parser-feedback retry loop (capped, never infinite) Pattern 4 — Type-coercion guardrails for money, dates, and percentages Pattern 5 — Semantic invariants across…
An AI agent human handoff failure rarely shows up in the dashboard. It shows up six weeks later, in a churn cohort, in a Trustpilot review, in the legal team’s inbox. The automation worked. The handoff to the human did not. And by the time anyone notices, the customer has already left. We have spent…
Table of Contents Why AI Agent Continuous Evaluation Quietly Falls Apart Pattern 1: Layered Golden Dataset Evaluation Pattern 2: Pre-Deploy Quality Gates in CI/CD for AI Agents Pattern 3: Multi-Judge Consensus for Prompt Regression Testing Pattern 4: Shadow Evaluation Against Live Traffic Pattern 5: Failure Replay Loops Pattern 6: Cost-Tiered Evaluation Budgets Pattern 7: Eval-Driven…
Table of Contents Why AI Agent Replanning Quietly Breaks in Production Pattern 1: Plan Invalidation Triggers (Replan vs. Retry) Pattern 2: Cost-Bounded Replanning Budgets Pattern 3: Cycle Detection for Agent Loop Prevention Pattern 4: Plan Diff Verification for Safe Plan Recovery Pattern 5: Hierarchical Planner-Executor Split for Multi-Step Agent Planning Pattern 6: Invariant Carryover Across…
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